In this notebook we look at expression of potential markers for tumor cell states in two libraries from SCPCP000015: SCPCL000822 and SCPCL000824. The expression of specific marker genes are then used to classify tumor cells into three different tumor cell states: EWS-low, EWS-mid, EWS-high, and EWS-high-proliferative.

We did this by:

Setup

suppressPackageStartupMessages({
  # load required packages
  library(SingleCellExperiment)
  library(ggplot2)
})

# Set default ggplot theme
theme_set(
  theme_classic()
)

# set seed
set.seed(2024)
# The base path for the OpenScPCA repository, found by its (hidden) .git directory
repository_base <- rprojroot::find_root(rprojroot::is_git_root)

# The current data directory, found within the repository base directory
data_dir <- file.path(repository_base, "data", "current", "SCPCP000015")

# The path to this module
module_base <- file.path(repository_base, "analyses", "cell-type-ewings")

# path to marker genes file
marker_genes_file <- file.path(module_base, "references", "tumor-cell-state-markers.tsv")

# gene signatures
aynaud_file <- file.path(module_base, "references", "gene_signatures", "aynaud-ews-targets.tsv")
wrenn_file <- file.path(module_base, "references","gene_signatures", "wrenn-nt5e-genes.tsv")

# path to save annotations tsv in results 
results_dir <- file.path(module_base, "results", "tumor-cell-state-classifications")
fs::dir_create(results_dir)
# source in helper functions: plot_density() and calculate_sum_markers()
validation_functions <- file.path(module_base, "scripts", "utils", "tumor-validation-helpers.R")
source(validation_functions)

Prepare data

# sample and library ids
sample_ids <- c("SCPCS000490", "SCPCS000492")
library_ids <- c("SCPCL000822", "SCPCL000824")

# define input sce files
sce_file_names <- glue::glue("{library_ids}_processed.rds")
sce_files <- file.path(data_dir, sample_ids, sce_file_names) |>
  purrr::set_names(library_ids)

# path to cell type classification files
singler_results_dir <- file.path(module_base, "results", "aucell_singler_annotation")
classification_file_names <- glue::glue("{library_ids}_singler-classifications.tsv")
classification_files <- file.path(singler_results_dir, sample_ids, classification_file_names) |>
  purrr::set_names(library_ids)

# path to clustering results files
cluster_results_dir <- file.path(module_base, "results", "clustering", sample_ids)
cluster_file_names <- glue::glue("{library_ids}_cluster-results.tsv")
cluster_files <- file.path(cluster_results_dir, cluster_file_names) |>
  purrr::set_names(library_ids)

Below we set up the data for plotting throughout this notebook.

  1. The output from aucell-singler-annotation.sh is read in and annotations are lumped so that we can easily plot the top cell types.
  2. Clustering results from evaluate-clusters.sh is read in and subset to the desired parameters we would like to use for assigning clusters (leiden, modularity, resolution 0.5, nearest neighbors 20).
  3. The marker genes for tumor cell states and custom gene signatures are used to calculate the mean of all marker genes in each gene set for each library.
  4. UMAP embeddings for each library are pulled out into a data frame and combined with SingleR annotations, cluster assignments, and marker gene expression for all marker gene sets.
# read in both SCEs
sce_list <- sce_files |>
  purrr::map(readr::read_rds)

# read in classification data frame with SingleR results and combine into one
classification_df <- classification_files |>
  purrr::map(readr::read_tsv) |>
  dplyr::bind_rows(.id = "library_id") |> 
  dplyr::mutate(
    # first grab anything that is tumor and label it tumor
    # NA should be unknown
    singler_annotation = dplyr::case_when(
      stringr::str_detect(singler_annotation, "tumor") ~ "tumor",
      is.na(singler_annotation) ~ "unknown", # make sure to separate out unknown labels
      .default = singler_annotation
    ) |>
      forcats::fct_relevel("tumor", after = 0),
    # get the top cell types for plotting later
    singler_lumped = singler_annotation |>
      forcats::fct_lump_n(7, other_level = "All remaining cell types", ties.method = "first") |>
      forcats::fct_infreq() |>
      forcats::fct_relevel("All remaining cell types", after = Inf)
  )

# read in clustering results and select cluster assignments of interest 
# here we keep leiden-mod, 0.5 res, 20 nn for both libraries 
cluster_df <- cluster_files |> 
  purrr::map(readr::read_tsv) |> 
  dplyr::bind_rows(.id = "library_id") |> 
  dplyr::filter(
    cluster_method == "leiden_mod",
    nn == 20,
    resolution == 0.5
  ) |> 
  dplyr::select(
    barcodes = cell_id,
    library_id, 
    cluster
  )
# read in marker genes table
marker_genes_df <- readr::read_tsv(marker_genes_file, show_col_types = FALSE) |>
  dplyr::select(cell_state, ensembl_gene_id, gene_symbol) 

# get individual gene signatures
aynaud_genes <- readr::read_tsv(aynaud_file) |> 
  dplyr::mutate(cell_state = "aynaud-ews-high") |>
  tidyr::drop_na()

wrenn_genes <- readr::read_tsv(wrenn_file) |> 
  dplyr::mutate(cell_state = "wrenn-ews-low") |>
  tidyr::drop_na()

# combine all genes into a single df 
all_markers_df <- dplyr::bind_rows(list(marker_genes_df, aynaud_genes, wrenn_genes))

# get list of all cell states/ gene lists 
cell_states <- unique(all_markers_df$cell_state)

# get the mean expression of all genes for each cell state
gene_exp_df <- sce_list |>
  purrr::map(\(sce) {
    cell_states |>
      purrr::map(\(state){
        calculate_mean_markers(all_markers_df, sce, state, cell_state)
      }) |>
      purrr::reduce(dplyr::inner_join, by = "barcodes")
  }) |>
  dplyr::bind_rows(.id = "library_id")

# get umap embeddings and combine into a data frame with gene exp, cluster, and cell type assignments 
umap_df <- sce_list |>
  purrr::map(\(sce){
    df <- sce |>
      scuttle::makePerCellDF(use.dimred = "UMAP") |>
      # replace UMAP.1 with UMAP1 and get rid of excess columns
      dplyr::select(barcodes, UMAP1 = UMAP.1, UMAP2 = UMAP.2)
  }) |>
  dplyr::bind_rows(.id = "library_id") |>
  # add in classifications 
  dplyr::left_join(classification_df, by = c("library_id", "barcodes")) |> 
  dplyr::left_join(gene_exp_df, by = c("barcodes", "library_id")) |> 
  # add in cluster assignments 
  dplyr::left_join(cluster_df, by = c("barcodes", "library_id"))

Identify tumor cells

To label tumor cell states, we first want to pull out the cells that are tumor cells and then look at the expression of marker genes for each tumor cell state in those cells. We have used a variety of methods to identify tumor cells so we will look at the output from two orthogonal approaches to identify the group of tumor cells we want to further classify into cell states. Let’s start by looking at the intersection of tumor cells assigned by the aucell-singler-annotation.sh workflow and the clusters obtained from the evaluate-clusters.sh workflow that have high expression of tumor marker genes.

Below are two plots that we generated as part of the evaluate-clusters.sh workflow that tells us which clusters express tumor marker genes for each of these libraries. We can use this information to help identify tumor cells to classify.

SCPCL000822
SCPCL000822
SCPCL000824
SCPCL000824
library_ids |>
  purrr::map(\(id){
    
    p1 <- umap_df |>
      dplyr::filter(library_id == id) |>
      ggplot(aes(x = UMAP1, y = UMAP2, color = singler_lumped)) +
      geom_point(alpha = 0.5, size = 0.1) +
      theme(
        aspect.ratio = 1
      ) +
      scale_color_brewer(palette = "Dark2") +
      labs(title = id) +
      guides(color = guide_legend(override.aes = list(alpha = 1, size = 1.5)))
    
    p2 <- umap_df |>
      dplyr::filter(library_id == id) |>
      ggplot(aes(x = UMAP1, y = UMAP2, color = as.factor(cluster))) +
      geom_point(alpha = 0.5, size = 0.1) +
      theme(
        aspect.ratio = 1
      ) +
      #scale_color_brewer(palette = "Dark2") +
      labs(title = id, color = "cluster") +
      guides(color = guide_legend(override.aes = list(alpha = 1, size = 1.5)))
    
    patchwork::wrap_plots(p1, p2, nrow = 2)
  })
## [[1]]
## Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_point()`).

## 
## [[2]]
## Warning: Removed 1 row containing missing values or values outside the scale range (`geom_point()`).

Looking at the above plots, we can assign some general cell types using clusters. This is the same approach we took in 05-cluster-exploration.Rmd but with slightly different parameters. Based on the UMAPs and the density plots we see that tumor cells are in cluster 1, 3, and 4 for SCPCL000822 and cluster 2, 3, 4, 5, 6, and 7 for SCPCL000824.

cluster_classification_df <- umap_df |> 
  dplyr::mutate(
    cluster_classification = dplyr::case_when(
      ############## Library SCPCL000822 ###############
      library_id == "SCPCL000822" & cluster %in% c(1, 3, 4) ~ "tumor",
      library_id == "SCPCL000822" & cluster == 6 ~ "endothelial cell",
      # cluster 2 has predominantly chondrocytes and has MSC marker gene expression
      library_id == "SCPCL000822" & cluster == 2 ~ "chondrocyte",
      # clusters with high "immune" marker gene expression are macrophage
      library_id == "SCPCL000822" & cluster == 5 ~ "macrophage",
      # cluster 8 has high MSC marker gene expression and a mix of MSC like cell types
      library_id == "SCPCL000822" & cluster == 7 ~ "mesenchymal-like cell",
      ############## Library SCPCL000824 ###############
      library_id == "SCPCL000824" & cluster %in% c(2, 3, 4, 5, 6, 7) ~ "tumor", 
      library_id == "SCPCL000824" & cluster == 8 ~ "endothelial cell",
      # cluster 9 has high immune markers and is mostly macrohpages
      library_id == "SCPCL000824" & cluster == 9 ~ "macrophage",
      # cluster 1 has high MSC marker gene expression and a mix of MSC like cell types
      library_id == "SCPCL000824" & cluster == 1 ~ "mesenchymal-like cell",
      # if they didn't have high expression of any markers, we label them as unknown
      .default = "unknown"
    )
  )
library_ids |>
  purrr::map(\(id){
    cluster_classification_df |>
      dplyr::filter(library_id == id) |>
      ggplot(aes(x = UMAP1, y = UMAP2, color = cluster_classification)) +
      geom_point(alpha = 0.5, size = 0.1) +
      theme(
        aspect.ratio = 1
      ) +
      labs(title = id) +
      scale_color_brewer(palette = "Dark2") +
      guides(color = guide_legend(override.aes = list(alpha = 1, size = 1.5)))
  })
## [[1]]

## 
## [[2]]

Looking at this I think the cells that are classified as tumor cells using the cluster assignments mostly line up with the SingleR annotations. So we can label any cells from clusters that have high tumor marker gene expression as tumor cells.

Look at marker gene expression for tumor cell states in all cells

Below we look at the expression of genes for the tumor cell states across all cells. This will show us how the expression of these gene sets varies across cells in each library and help ensure we have the correct population of tumor cells.

The value for each of the marker gene sets corresponds to the mean of the expression of all genes in the given gene list.

The gene sets that we use are:

  • EWS-low: Marker genes from tumor-cell-state-markers.tsv that correspond to the EWS-low cell state.
  • EWS-high: Marker genes from tumor-cell-state-markers.tsv that correspond to the EWS-high cell state.
  • proliferative : Marker genes from tumor-cell-state-markers.tsv that correspond to the proliferative cell state. Note that a lot of the Ewing sarcoma literature argues that EWS-FLI1 high cells are proliferative.
  • aynaud-ews-high: Marker genes from gene_signatures/aynaud-ews-targets.tsv that correspond to genes activated by EWS-FLI1.
  • wrenn-ews-low: Marker genes from gene_signatures/wrenn-nt5e-genes.tsv that correspond to genes repressed by EWS-FLI1.
# pull out columns that have sum of marker gene expression 
marker_gene_cols <- colnames(cluster_classification_df)[stringr::str_detect(colnames(cluster_classification_df), "_mean$")]

# look at the expression of all gene sets across both libraries 
marker_gene_cols |>
  purrr::map(\(geneset){
    cluster_classification_df |>
      ggplot(aes(x = UMAP1, y = UMAP2, color = !!sym(geneset))) +
      geom_point(alpha = 0.5, size = 0.1) +
      facet_wrap(vars(library_id)) +
      scale_color_viridis_c() +
      theme(
        aspect.ratio = 1
      )
  })
## [[1]]

## 
## [[2]]

## 
## [[3]]

## 
## [[4]]

## 
## [[5]]

It looks like there’s a cluster of cells in both libraries that have expression of the EWS-low and wrenn-ews-low signatures. Those cells correspond to the cells that we are labeling as “chondrocyte” in SCPCL000822 and “mesenchymal-like” in SCPCL000824. The chondrocytes also express high markers of mesenchymal-like genes and the literature talks about tumor cells with low EWS-FLI1 expression being “MSC-like”. I think there’s a possibility that these are tumor cells, so let’s relabel them.

# create a new tumor classification that takes all tumor clusters plus the clusters with EWS-FLI1 low expression 
cluster_classification_df <- cluster_classification_df |> 
  dplyr::mutate(
    tumor_classification = dplyr::case_when(
      cluster_classification == "tumor" ~ "tumor",
      library_id == "SCPCL000822" & cluster_classification == "chondrocyte" ~ "tumor",
      library_id == "SCPCL000824" & cluster_classification == "mesenchymal-like cell" ~ "tumor",
      .default = cluster_classification
    )
  )

Classify tumor cell states

Now that we have identified a subset of cells that are tumor cells, we can attempt to group them into various cell states. The main thing that we are interested in is grouping cells into levels of EWS-FLI1 expression. Let’s start by looking at the expression of the gene sets across all tumor cells.

To do this, we will make a density plot that looks at gene set expression by cluster using the cluster assignments calculated using all cells.

tumor_cells_df <- cluster_classification_df |> 
  dplyr::filter(tumor_classification == "tumor")
# density plot showing expression of each gene set by cluster
library_ids |> 
  purrr::map(\(id){
    
    density_df <- tumor_cells_df |> 
      dplyr::filter(library_id == id)
    
    marker_gene_cols |>
    purrr::map(\(column){
      plot_density(
        density_df,
        column,
        "cluster"
      ) 
    }) |>
    patchwork::wrap_plots(ncol = 1) +
      patchwork::plot_annotation(id)
    
  })
## [[1]]

## 
## [[2]]

A few notes from this:

Sub cluster tumor cells

We would expect that tumor cells that are in similar cell states would cluster together. So here I am going to take just the tumor cells and re-assign clusters. I’m using the same parameters we use for the whole sample, leiden, modularity, resolution of 0.5, and nearest neighbors of 20. I think if we really wanted to be stringent we would evaluate clusters here and find the best parameters to use, but I think starting with these is good.

# get a data frame of all tumor cells and re assigned clusters 
subcluster_df <- sce_list |>
  purrr::imap(\(sce, id){
    
    # pull out the barcodes that are from tumor cells
    tumor_cells <- tumor_cells_df |> 
      dplyr::filter(library_id == id) |> 
      dplyr::pull(barcodes)
    
    # filter the sce to only those barcodes
    tumor_sce <- sce[ , tumor_cells]
    
    # grab pcs for clustering
    pcs <- reducedDim(tumor_sce)
    
    # get the clusters 
    clusters <- bluster::clusterRows(
      pcs,
      bluster::NNGraphParam(
        k = 20,
        type = "jaccard",
        cluster.fun = "leiden",
        cluster.args = list(objective_function = "modularity",
                            resolution = 0.5)
      )
    )
    
    # combine into a data frame with barcodes
    cluster_df <- data.frame(
      barcodes = rownames(pcs),
      sub_cluster = clusters
    )
  }) |> 
  # get clusters for both samples in one data frame
  dplyr::bind_rows(.id = "library_id")

# add clusters to tumor cells df for plotting
tumor_cells_df <- tumor_cells_df |> 
  dplyr::left_join(subcluster_df, by = c("library_id", "barcodes"))

The first thing we will do is plot the cluster assignments on the UMAP.

library_ids |>
  purrr::map(\(id){
    tumor_cells_df |>
      dplyr::filter(library_id == id) |>
      ggplot(aes(x = UMAP1, y = UMAP2, color = sub_cluster)) +
      geom_point(alpha = 0.5, size = 0.1) +
      theme(
        aspect.ratio = 1
      ) +
      labs(title = id) +
      scale_color_brewer(palette = "Dark2") +
      guides(color = guide_legend(override.aes = list(alpha = 1, size = 1.5)))
  })
## [[1]]

## 
## [[2]]

Now we will look at the marker gene expression across all clusters for each sample, first using a density plot and then heatmaps.

library_ids |> 
  purrr::map(\(id){
    
    density_df <- tumor_cells_df |> 
      dplyr::filter(library_id == id)
    
    marker_gene_cols |>
    purrr::map(\(column){
      plot_density(
        density_df,
        column,
        "sub_cluster"
      ) 
    }) |>
    patchwork::wrap_plots(ncol = 1) +
      patchwork::plot_annotation(id)
    
  })
## [[1]]

## 
## [[2]]

In all of the heatmaps below, the annotation column is the sub_cluster.

library_ids |> 
  purrr::map(\(id){
    
    filtered_df <- tumor_cells_df |> 
      dplyr::filter(library_id == id)
    
    full_celltype_heatmap(filtered_df, marker_gene_cols, "sub_cluster")
    
  })
## [[1]]

## 
## [[2]]

Let’s make the same heatmap, but remove the column clustering.

library_ids |> 
  purrr::map(\(id){
    
    filtered_df <- tumor_cells_df |> 
      dplyr::filter(library_id == id) |> 
      dplyr::arrange(sub_cluster)
    
    full_celltype_heatmap(filtered_df, marker_gene_cols, "sub_cluster", cluster_columns = FALSE)
    
  })
## [[1]]

## 
## [[2]]

It looks like for both samples we have some clear division between EWS high and EWS low cells. Additionally, there are some cells with really strong expression of the EWS high markers and others that look like the genes are expressed but to a lesser degree. This fits with the findings from the literature that EWS-FLI1 expression lies on a continuum.

Between these two plots I think we can try and group cells into EWS low, EWS middle, and EWS high cells.

The last thing we’ll do is look at any cells that could be marked as “proliferative”. SCPCL000822 doesn’t appear to have very much expression of the proliferative markers, but SCPCL000824 does seem to have one cluster, cluster 2, that has expression of the proliferative cells. Perhaps we should label those cells separately as EWS-high-proliferative.

Based on this I would make the following assignments:

SCPCL000822:

EWS-FLI1 low - 2 EWS-FLI1 middle - 1 & 4 EWS-FLI1 high - 3

SCPCL000824 :

EWS-FLI1 low - 1 EWS-FLI1 middle - 6 EWS-FLI1 high - 3, 4, 5, 7 EWS-FLI1 high proliferative - 2

# categorize tumor cells based on EWS expression 
tumor_cells_df <- tumor_cells_df |> 
  dplyr::mutate(
    cell_state = dplyr::case_when(
      library_id == "SCPCL000822" & sub_cluster == 2 ~ "EWS-low",
      library_id == "SCPCL000822" & sub_cluster %in% c(1, 4) ~ "EWS-mid",
      library_id == "SCPCL000822" & sub_cluster == 3 ~ "EWS-high",
      library_id == "SCPCL000824" & sub_cluster == 1 ~ "EWS-low",
      library_id == "SCPCL000824" & sub_cluster == 6 ~ "EWS-mid",
      library_id == "SCPCL000824" & sub_cluster == 2 ~ "EWS-high-proliferative",
      library_id == "SCPCL000824" & sub_cluster %in% c(3, 4, 5, 7) ~ "EWS-high",
    )
  )

Let’s re-plot the heatmap but label by tumor cell state rather than cluster. In this heatmap the annotation column is cell_state.

library_ids |> 
  purrr::map(\(id){
    
    filtered_df <- tumor_cells_df |> 
      dplyr::filter(library_id == id)|> 
      dplyr::arrange(cell_state)
    
    full_celltype_heatmap(filtered_df, marker_gene_cols, "cell_state", cluster_columns = FALSE)
    
  })
## [[1]]

## 
## [[2]]

I think that looks pretty reasonable. I’m not sure we can really make more definitive calls given the gene lists that we have, unless we use fancier approaches. We could try something like AUCell, but without a nice bimodal distribution, I don’t think we would get anything but low/high cells.

Combined classifications

As a final step, we’ll add the tumor cell states back into the full object and look at them on the UMAP. Here I am plotting the classification of cells based on the cluster because otherwise the normal cells get a little wild with colors and I want to be able to see visualize the tumor cells.

# select the columns we want to join back into the main classification df 
cell_state_classifications <- tumor_cells_df |> 
  dplyr::select(library_id, barcodes, cell_state, sub_cluster)

# add cell states to classification 
final_classification_df <- cluster_classification_df |> 
  dplyr::left_join(cell_state_classifications, by = c("library_id", "barcodes")) |> 
  dplyr::mutate(full_classification = dplyr::if_else(is.na(cell_state), singler_lumped, cell_state),
                cluster_classification = dplyr::if_else(is.na(cell_state), cluster_classification, cell_state))

# make some umaps 
library_ids |>
  purrr::map(\(id){
    final_classification_df |>
      dplyr::filter(library_id == id) |>
      ggplot(aes(x = UMAP1, y = UMAP2, color = cluster_classification)) +
      geom_point(alpha = 0.5, size = 0.1) +
      theme(
        aspect.ratio = 1
      ) +
      labs(title = id) +
      scale_color_brewer(palette = "Dark2") +
      guides(color = guide_legend(override.aes = list(alpha = 1, size = 1.5)))
  })
## [[1]]

## 
## [[2]]

# export the final classifications in case we want to use them in the future 
# save all classification columns 
final_classification_df |> 
  split(final_classification_df$library_id) |> 
  purrr::iwalk(\(df, id){
    
    filename <- glue::glue("{id}_tumor-cell-state-annotations.tsv")
    
    df |> 
      dplyr::select(
        library_id,
        barcodes,
        singler_annotation,
        aucell_annotation, 
        cluster, 
        cluster_classification, 
        tumor_sub_cluster = sub_cluster, 
        tumor_cell_state = cell_state,
        full_classification 
      ) |> 
      readr::write_tsv(file.path(results_dir, filename))
    
  })

Conclusions

Session info

# record the versions of the packages used in this analysis and other environment information
sessionInfo()
## R version 4.4.2 (2024-10-31)
## Platform: aarch64-apple-darwin20
## Running under: macOS Sequoia 15.2
## 
## Matrix products: default
## BLAS:   /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib 
## LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.0
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## time zone: America/Chicago
## tzcode source: internal
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices datasets  utils     methods   base     
## 
## other attached packages:
##  [1] ggplot2_3.5.1               SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0 Biobase_2.64.0              GenomicRanges_1.56.1        GenomeInfoDb_1.40.1        
##  [7] IRanges_2.38.1              S4Vectors_0.42.1            BiocGenerics_0.50.0         MatrixGenerics_1.16.0       matrixStats_1.3.0          
## 
## loaded via a namespace (and not attached):
##  [1] tidyselect_1.2.1          viridisLite_0.4.2         dplyr_1.1.4               farver_2.1.2              fastmap_1.2.0             bluster_1.14.0           
##  [7] digest_0.6.36             lifecycle_1.0.4           cluster_2.1.6             magrittr_2.0.3            compiler_4.4.2            rlang_1.1.4              
## [13] sass_0.4.9                tools_4.4.2               igraph_2.0.3              utf8_1.2.4                yaml_2.3.10               knitr_1.48               
## [19] S4Arrays_1.4.1            labeling_0.4.3            bit_4.0.5                 DelayedArray_0.30.1       RColorBrewer_1.1-3        abind_1.4-5              
## [25] BiocParallel_1.38.0       withr_3.0.0               purrr_1.0.2               grid_4.4.2                fansi_1.0.6               beachmat_2.20.0          
## [31] colorspace_2.1-1          iterators_1.0.14          scales_1.3.0              cli_3.6.3                 rmarkdown_2.27            crayon_1.5.3             
## [37] generics_0.1.3            rjson_0.2.21              httr_1.4.7                tzdb_0.4.0                DelayedMatrixStats_1.26.0 scuttle_1.14.0           
## [43] cachem_1.1.0              stringr_1.5.1             zlibbioc_1.50.0           parallel_4.4.2            BiocManager_1.30.23       XVector_0.44.0           
## [49] vctrs_0.6.5               Matrix_1.7-0              jsonlite_1.8.8            GetoptLong_1.0.5          hms_1.1.3                 patchwork_1.2.0          
## [55] BiocNeighbors_1.22.0      bit64_4.0.5               clue_0.3-65               foreach_1.5.2             jquerylib_0.1.4           tidyr_1.3.1              
## [61] glue_1.7.0                codetools_0.2-20          shape_1.4.6.1             stringi_1.8.4             gtable_0.3.5              UCSC.utils_1.0.0         
## [67] ComplexHeatmap_2.20.0     munsell_0.5.1             tibble_3.2.1              pillar_1.9.0              htmltools_0.5.8.1         circlize_0.4.16          
## [73] GenomeInfoDbData_1.2.12   R6_2.5.1                  sparseMatrixStats_1.16.0  doParallel_1.0.17         rprojroot_2.0.4           vroom_1.6.5              
## [79] evaluate_0.24.0           lattice_0.22-6            readr_2.1.5               highr_0.11                png_0.1-8                 renv_1.0.7               
## [85] bslib_0.8.0               Rcpp_1.0.13               SparseArray_1.4.8         xfun_0.46                 GlobalOptions_0.1.2       fs_1.6.4                 
## [91] forcats_1.0.0             pkgconfig_2.0.3